# Copyright (c) MEGVII Inc. and its affiliates. All Rights Reserved.
import math
from typing import Optional

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint

from .layers_quant import (DropPath, HybridEmbed, Mlp, PatchEmbed, to_2tuple,
                           trunc_normal_)
from .ptq import QAct, QConv2d, QIntLayerNorm, QIntSoftmax, QLinear

__all__ = [
    'swin_tiny_patch4_window7_224', 'swin_small_patch4_window7_224',
    'swin_base_patch4_window7_224'
]


def window_partition(x, window_size: int):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size,
               C)
    windows = x.permute(0, 1, 3, 2, 4,
                        5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size: int, H: int, W: int):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size,
                     window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self,
                 dim,
                 window_size,
                 num_heads,
                 qkv_bias=True,
                 attn_drop=0.,
                 proj_drop=0.,
                 quant=False,
                 calibrate=False,
                 cfg=None):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim**-0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - \
            coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(
            1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - \
            1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer('relative_position_index',
                             relative_position_index)

        # self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.qkv = QLinear(dim,
                           dim * 3,
                           bias=qkv_bias,
                           quant=quant,
                           calibrate=calibrate,
                           bit_type=cfg.BIT_TYPE_W,
                           calibration_mode=cfg.CALIBRATION_MODE_W,
                           observer_str=cfg.OBSERVER_W,
                           quantizer_str=cfg.QUANTIZER_W)
        self.qact1 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        self.qact_attn1 = QAct(quant=quant,
                               calibrate=calibrate,
                               bit_type=cfg.BIT_TYPE_A,
                               calibration_mode=cfg.CALIBRATION_MODE_A,
                               observer_str=cfg.OBSERVER_A,
                               quantizer_str=cfg.QUANTIZER_A)
        self.qact_table = QAct(quant=quant,
                               calibrate=calibrate,
                               bit_type=cfg.BIT_TYPE_A,
                               calibration_mode=cfg.CALIBRATION_MODE_A,
                               observer_str=cfg.OBSERVER_A,
                               quantizer_str=cfg.QUANTIZER_A)
        self.qact2 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)

        self.attn_drop = nn.Dropout(attn_drop)
        self.log_int_softmax = QIntSoftmax(
            log_i_softmax=cfg.INT_SOFTMAX,
            quant=quant,
            calibrate=calibrate,
            bit_type=cfg.BIT_TYPE_S,
            calibration_mode=cfg.CALIBRATION_MODE_S,
            observer_str=cfg.OBSERVER_S,
            quantizer_str=cfg.QUANTIZER_S)
        self.qact3 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        self.qact4 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        # self.proj = nn.Linear(dim, dim)
        self.proj = QLinear(dim,
                            dim,
                            quant=quant,
                            calibrate=calibrate,
                            bit_type=cfg.BIT_TYPE_W,
                            calibration_mode=cfg.CALIBRATION_MODE_W,
                            observer_str=cfg.OBSERVER_W,
                            quantizer_str=cfg.QUANTIZER_W)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask: Optional[torch.Tensor] = None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        x = self.qkv(x)
        x = self.qact1(x)
        qkv = x.reshape(B_, N, 3, self.num_heads,
                        C // self.num_heads).permute(2, 0, 3, 1, 4)
        # make torchscript happy (cannot use tensor as tuple)
        q, k, v = qkv[0], qkv[1], qkv[2]

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))
        attn = self.qact_attn1(attn)
        relative_position_bias_table_q = self.qact_table(
            self.relative_position_bias_table)
        relative_position_bias = relative_position_bias_table_q[
            self.relative_position_index.view(-1)].view(
                self.window_size[0] * self.window_size[1],
                self.window_size[0] * self.window_size[1],
                -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(
            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)
        attn = self.qact2(attn)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N,
                             N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.log_int_softmax(attn, self.qact2.quantizer.scale)
        else:
            attn = self.log_int_softmax(attn, self.qact2.quantizer.scale)

        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.qact3(x)
        x = self.proj(x)
        x = self.qact4(x)
        x = self.proj_drop(x)
        return x


class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self,
                 dim,
                 input_resolution,
                 num_heads,
                 window_size=7,
                 shift_size=0,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 quant=False,
                 calibrate=False,
                 cfg=None):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size'

        self.norm1 = norm_layer(dim)
        self.qact1 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        self.attn = WindowAttention(dim,
                                    window_size=to_2tuple(self.window_size),
                                    num_heads=num_heads,
                                    qkv_bias=qkv_bias,
                                    attn_drop=attn_drop,
                                    proj_drop=drop,
                                    quant=quant,
                                    calibrate=calibrate,
                                    cfg=cfg)

        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
        self.qact2 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A_LN,
                          observer_str=cfg.OBSERVER_A_LN,
                          quantizer_str=cfg.QUANTIZER_A_LN)
        self.norm2 = norm_layer(dim)
        self.qact3 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim,
                       hidden_features=mlp_hidden_dim,
                       act_layer=act_layer,
                       drop=drop,
                       quant=quant,
                       calibrate=calibrate,
                       cfg=cfg)
        self.qact4 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A_LN,
                          observer_str=cfg.OBSERVER_A_LN,
                          quantizer_str=cfg.QUANTIZER_A_LN)
        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size,
                              -self.shift_size), slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size,
                              -self.shift_size), slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            # nW, window_size, window_size, 1
            mask_windows = window_partition(img_mask, self.window_size)
            mask_windows = mask_windows.view(
                -1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0,
                                              float(-100.0)).masked_fill(
                                                  attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer('attn_mask', attn_mask)

    def forward(self, x, last_quantizer=None):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, 'input feature has wrong size'

        shortcut = x
        x = self.norm1(x, last_quantizer, self.qact1.quantizer)
        x = self.qact1(x)
        x = x.view(B, H, W, C)

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x,
                                   shifts=(-self.shift_size, -self.shift_size),
                                   dims=(1, 2))
        else:
            shifted_x = x

        # partition windows
        # nW*B, window_size, window_size, C
        x_windows = window_partition(shifted_x, self.window_size)
        # nW*B, window_size*window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)

        # W-MSA/SW-MSA
        # nW*B, window_size*window_size, C
        attn_windows = self.attn(x_windows, mask=self.attn_mask)

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size,
                                         self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H,
                                   W)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x,
                           shifts=(self.shift_size, self.shift_size),
                           dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = self.qact2(x)
        x = x + self.drop_path(
            self.mlp(
                self.qact3(
                    self.norm2(x, self.qact2.quantizer,
                               self.qact3.quantizer))))
        x = self.qact4(x)

        return x


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self,
                 input_resolution,
                 dim,
                 norm_layer=nn.LayerNorm,
                 quant=False,
                 calibrate=False,
                 cfg=None):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim

        self.norm = norm_layer(4 * dim)
        self.qact1 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        # self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.reduction = QLinear(4 * dim,
                                 2 * dim,
                                 bias=False,
                                 quant=quant,
                                 calibrate=calibrate,
                                 bit_type=cfg.BIT_TYPE_W,
                                 calibration_mode=cfg.CALIBRATION_MODE_W,
                                 observer_str=cfg.OBSERVER_W,
                                 quantizer_str=cfg.QUANTIZER_W)
        self.qact2 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A_LN,
                          observer_str=cfg.OBSERVER_A_LN,
                          quantizer_str=cfg.QUANTIZER_A_LN)

    def forward(self, x, last_quantizer=None):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, 'input feature has wrong size'
        assert H % 2 == 0 and W % 2 == 0, f'x size ({H}*{W}) are not even.'

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
        x = self.norm(x, last_quantizer, self.qact1.quantizer, 4)
        x = self.qact1(x)
        x = self.reduction(x)
        x = self.qact2(x)
        return x

    def extra_repr(self) -> str:
        return f'input_resolution={self.input_resolution}, dim={self.dim}'

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self,
                 dim,
                 input_resolution,
                 depth,
                 num_heads,
                 window_size,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 norm_layer=nn.LayerNorm,
                 downsample=None,
                 use_checkpoint=False,
                 quant=False,
                 calibrate=False,
                 cfg=None):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim,
                                 input_resolution=input_resolution,
                                 num_heads=num_heads,
                                 window_size=window_size,
                                 shift_size=0 if
                                 (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias,
                                 drop=drop,
                                 attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(
                                     drop_path, list) else drop_path,
                                 norm_layer=norm_layer,
                                 quant=quant,
                                 calibrate=calibrate,
                                 cfg=cfg) for i in range(depth)
        ])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution,
                                         dim=dim,
                                         norm_layer=norm_layer,
                                         quant=quant,
                                         calibrate=calibrate,
                                         cfg=cfg)
        else:
            self.downsample = None

    def forward(self, x, last_quantizer=None):
        for i, blk in enumerate(self.blocks):
            if not torch.jit.is_scripting() and self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                if i == 0:
                    x = blk(x, last_quantizer)
                else:
                    x = blk(x, self.blocks[i - 1].qact4.quantizer)
        if self.downsample is not None:
            x = self.downsample(x, self.blocks[-1].qact4.quantizer)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}'


class SwinTransformer(nn.Module):
    r""" Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self,
                 img_size=224,
                 patch_size=4,
                 in_chans=3,
                 num_classes=1000,
                 embed_dim=96,
                 depths=(2, 2, 6, 2),
                 num_heads=(3, 6, 12, 24),
                 window_size=7,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm,
                 ape=False,
                 patch_norm=True,
                 use_checkpoint=False,
                 quant=False,
                 calibrate=False,
                 input_quant=False,
                 cfg=None,
                 **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2**(self.num_layers - 1))
        self.mlp_ratio = mlp_ratio
        self.input_quant = input_quant
        self.cfg = cfg
        if input_quant:
            self.qact_input = QAct(quant=quant,
                                   calibrate=calibrate,
                                   bit_type=cfg.BIT_TYPE_A,
                                   calibration_mode=cfg.CALIBRATION_MODE_A,
                                   observer_str=cfg.OBSERVER_A,
                                   quantizer_str=cfg.QUANTIZER_A)
        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None,
            quant=quant,
            calibrate=calibrate,
            cfg=cfg)
        num_patches = self.patch_embed.num_patches
        self.patch_grid = self.patch_embed.grid_size

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(
                torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)
            self.qact1 = QAct(quant=quant,
                              calibrate=calibrate,
                              bit_type=cfg.BIT_TYPE_A,
                              calibration_mode=cfg.CALIBRATION_MODE_LN,
                              observer_str=cfg.OBSERVER_LN,
                              quantizer_str=cfg.QUANTIZER_LN)
        else:
            self.absolute_pos_embed = None

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
        ]  # stochastic depth decay rule

        # build layers
        layers = []
        for i_layer in range(self.num_layers):
            layers += [
                BasicLayer(
                    dim=int(embed_dim * 2**i_layer),
                    input_resolution=(self.patch_grid[0] // (2**i_layer),
                                      self.patch_grid[1] // (2**i_layer)),
                    depth=depths[i_layer],
                    num_heads=num_heads[i_layer],
                    window_size=window_size,
                    mlp_ratio=self.mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer +
                                                                   1])],
                    norm_layer=norm_layer,
                    downsample=PatchMerging if
                    (i_layer < self.num_layers - 1) else None,
                    use_checkpoint=use_checkpoint,
                    quant=quant,
                    calibrate=calibrate,
                    cfg=cfg)
            ]
        self.layers = nn.Sequential(*layers)

        self.norm = norm_layer(self.num_features)

        self.qact2 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.qact3 = QAct(quant=quant,
                          calibrate=calibrate,
                          bit_type=cfg.BIT_TYPE_A,
                          calibration_mode=cfg.CALIBRATION_MODE_A,
                          observer_str=cfg.OBSERVER_A,
                          quantizer_str=cfg.QUANTIZER_A)
        self.head = QLinear(self.num_features,
                            num_classes,
                            quant=quant,
                            calibrate=calibrate,
                            bit_type=cfg.BIT_TYPE_W,
                            calibration_mode=cfg.CALIBRATION_MODE_W,
                            observer_str=cfg.OBSERVER_W,
                            quantizer_str=cfg.QUANTIZER_W
                            ) if num_classes > 0 else nn.Identity()

        self.act_out = QAct(quant=quant,
                            calibrate=calibrate,
                            bit_type=cfg.BIT_TYPE_A,
                            calibration_mode=cfg.CALIBRATION_MODE_A,
                            observer_str=cfg.OBSERVER_A,
                            quantizer_str=cfg.QUANTIZER_A)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = QLinear(self.num_features,
                            num_classes,
                            quant=quant,
                            calibrate=calibrate,
                            bit_type=cfg.BIT_TYPE_W,
                            calibration_mode=cfg.CALIBRATION_MODE_W,
                            observer_str=cfg.OBSERVER_W,
                            quantizer_str=cfg.QUANTIZER_W
                            ) if num_classes > 0 else nn.Identity()

    def model_quant(self):
        for m in self.modules():
            if type(m) in [QConv2d, QLinear, QAct, QIntSoftmax]:
                m.quant = True
            if self.cfg.INT_NORM:
                if type(m) in [QIntLayerNorm]:
                    m.mode = 'int'

    def model_dequant(self):
        for m in self.modules():
            if type(m) in [QConv2d, QLinear, QAct, QIntSoftmax]:
                m.quant = False

    def model_open_calibrate(self):
        for m in self.modules():
            if type(m) in [QConv2d, QLinear, QAct, QIntSoftmax]:
                m.calibrate = True

    def model_open_last_calibrate(self):
        for m in self.modules():
            if type(m) in [QConv2d, QLinear, QAct, QIntSoftmax]:
                m.last_calibrate = True

    def model_close_calibrate(self):
        for m in self.modules():
            if type(m) in [QConv2d, QLinear, QAct, QIntSoftmax]:
                m.calibrate = False

    def forward_features(self, x):
        if self.input_quant:
            x = self.qact_input(x)
        x = self.patch_embed(x)
        if self.absolute_pos_embed is not None:
            x = x + self.absolute_pos_embed
            x = self.qact1(x)
        x = self.pos_drop(x)
        for i, layer in enumerate(self.layers):
            last_quantizer = self.patch_embed.qact.quantizer if i == 0 else self.layers[
                i - 1].downsample.qact2.quantizer
            x = layer(x, last_quantizer)

        x = self.norm(x, self.layers[-1].blocks[-1].qact4.quantizer,
                      self.qact2.quantizer)  # B L C
        x = self.qact2(x)

        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = self.qact3(x)

        x = torch.flatten(x, 1)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        x = self.act_out(x)
        return x


def swin_tiny_patch4_window7_224(pretrained=False,
                                 quant=False,
                                 calibrate=False,
                                 cfg=None,
                                 **kwargs):
    """ Swin-T @ 224x224, trained ImageNet-1k
    """
    model = SwinTransformer(patch_size=4,
                            window_size=7,
                            embed_dim=96,
                            depths=(2, 2, 6, 2),
                            num_heads=(3, 6, 12, 24),
                            norm_layer=QIntLayerNorm,
                            quant=quant,
                            calibrate=calibrate,
                            input_quant=True,
                            cfg=cfg,
                            **kwargs)
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url=
            'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',
            map_location='cpu',
            check_hash=True)
        model.load_state_dict(checkpoint['model'], strict=False)
    return model


def swin_small_patch4_window7_224(pretrained=False,
                                  quant=False,
                                  calibrate=False,
                                  cfg=None,
                                  **kwargs):
    """ Swin-S @ 224x224, trained ImageNet-1k
    """
    model = SwinTransformer(patch_size=4,
                            window_size=7,
                            embed_dim=96,
                            depths=(2, 2, 18, 2),
                            num_heads=(3, 6, 12, 24),
                            norm_layer=QIntLayerNorm,
                            quant=quant,
                            calibrate=calibrate,
                            input_quant=True,
                            cfg=cfg,
                            **kwargs)
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url=
            'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',
            map_location='cpu',
            check_hash=True)
        model.load_state_dict(checkpoint['model'], strict=False)
    return model


def swin_base_patch4_window7_224(pretrained=False,
                                 quant=False,
                                 calibrate=False,
                                 cfg=None,
                                 **kwargs):
    """ Swin-B @ 224x224, trained ImageNet-1k
    """
    model = SwinTransformer(patch_size=4,
                            window_size=7,
                            embed_dim=128,
                            depths=(2, 2, 18, 2),
                            num_heads=(4, 8, 16, 32),
                            norm_layer=QIntLayerNorm,
                            quant=quant,
                            calibrate=calibrate,
                            input_quant=True,
                            cfg=cfg,
                            **kwargs)
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url=
            'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth',
            map_location='cpu',
            check_hash=True)
        model.load_state_dict(checkpoint['model'], strict=False)
    return model
